1. Field of the Invention
The present invention relates to learning type waveform recognizers that recognize waveforms of signals such as time series signals based on learning.
2. Description of the Related Art
As a prior technique for recognizing waveforms of signals there has been proposed apparatus that compares the waveform of an input signal with prepared templates of waveforms and outputs the identification number of a template having a minimum error.
FIG. 15 shows a block diagram of a prior waveform recognizor of this type. A reference numeral 93 denotes a template comparator, 91 denotes an input terminal of the comparator 93 for input signals, 92 denotes an output terminal of the comparator 93 for output signals. 94 to 97 denote prepayred templates of waveform patterns, 99 denotes a learning section, and 98 denotes an input terminal of the learning section 99 for teacher signals.
A signal to be recognized is input to the template comparator 93 through the input terminal 91. As shown in FIG. 16, the template comparator 93 successively shifts the input signal to frame the parts of the signal to be compared, compares each part with each of templates 94 to 97, and outputs the number of a template having the minimum error through the output terminal 93. If the output result is wrong, then the number of the template to which the input signal belongs is input to the learning section 99 through the input terminal 98, and the prepared templates 94 to 97 are revised. In this way the waveform recognizer learns and improves its accuracy of recognition.
The prior waveform recognizer described above has to compare each input signal with every template and successively shift the input signal, and therefore the comparison process takes a long time. Moreover, since templates have to be prepared as many as the number of the categories of waveform patterns to be classified into, mass storage is necessary for templates. Further, since learning is performed by revising templates, learning process also requires a long time.